LangChain integration for pgvector and Timescale Vector

LangChain is a popular framework for development applications powered by LLMs. Timescale Vector has a native LangChain integration, enabling you to use Timescale Vector as a vector store and leverage all its capabilities in your applications built with LangChain.

Here are resources about using Timescale Vector with LangChain:

• Getting started with LangChain and Timescale Vector: You'll learn how to use Timescale Vector for (1) semantic search, (2) time-based vector search, (3) self-querying, and (4) how to create indexes to speed up queries.
• PostgreSQL Self Querying: Learn how to use Timescale Vector with self-querying in LangChain.
• LangChain template: RAG with conversational retrieval: This template is used for conversational retrieval, which is one of the most popular LLM use-cases. It passes both a conversation history and retrieved documents into an LLM for synthesis.
• LangChain template: RAG with time-based search and self-query retrieval: This template shows how to use timescale-vector with the self-query retriever to perform hybrid search on similarity and time. This is useful any time your data has a strong time-based component.
• Learn more about Timescale Vector and LangChain: A blog post about the unique capabilities that Timescale Vector brings to the LangChain ecosystem.